hmmm (version 1.0.0)

hmmm.chibar: chi-bar statistics test for hmm models

Description

Function to simulate weights and pvalues of a chi-bar distributed statistic for testing hypotheses of inequality constraints on parameters of hmm models. The models in inputs are objects inheriting from class hmmmfit.

Usage

hmmm.chibar(nullfit, disfit, satfit, repli = 6000)

Arguments

nullfit

The estimated model with inequalities turned into equalities, a result of `hmmm.mlfit'

disfit

The estimated model with inequalities, a result of `hmmm.mlfit'

satfit

The estimated model without inequalities, a result of `hmmm.mlfit'

repli

Number of simulations

Value

• A list with the statistics test of type A and B (Silvapulle and Sen, 2005, pg. 61) and their simulated pvalues.

Details

The method "Simulation 2" described in Silvapulle and Sen, 2005, pg. 79 is used.

References

Silvapulle MJ, Sen PK (2005) Constrained statistical inference, Wiley, New Jersey.

See Also

chibar, summary.hmmmchibar, print.hmmmchibar

Examples

data(polbirth)
# 1 = Politics; 2 = Birthcontrol
y<-getnames(polbirth,st=12,sep=";")                     
names<-c("Pol","Birth")
marglist<-c("l-m","m-l","l-l")
marginals<-marg.list(marglist,mflag="m")
ineq<-list(marg=c(1,2),int=list(c(1,2)),types=c("l","l"))

# definition of the model with inequalities on interactions in ineq
model<-hmmm.model(marg=marginals,dismarg=list(ineq),lev=c(7,4),
strata=1,X=diag(1,27),names=names)

# saturated model
msat<-hmmm.mlfit(y,model)

# model with non-negative local log-odds ratios: "Likelihood ratio monotone dependence model"
mlr<-hmmm.mlfit(y,model,noineq=FALSE)

# model with null local log-odds ratios: "Stochastic independence model"
model0<-hmmm.model(marg=marginals,lev=c(7,4),sel=c(10:27),names=names)
mnull<-hmmm.mlfit(y,model0)

# HYPOTHESES TESTED:
#     testA --> H0=(mnull model) vs H1=(mlr model)
#     testB --> H0=(mlr model) vs H1=(msat model)

P<-hmmm.chibar(nullfit=mnull,disfit=mlr,satfit=msat)
summary(P)